统一最小二乘规则的单幅图像超分辨算法

基于机器学习的超分辨方法是一个很有发展前景的单幅图像超分辨方法,稀疏表达和字典学习是其中的研究热点。针对比较耗时的字典训练与恢复精度不高图像重建,从减小低分辨率(LR)和高分辨率(HR)特征空间之间差异性的角度提出了一种使用迭代最小二乘字典学习算法(ILS-DLA),并使用锚定邻域回归(ANR)进行图像重建的单幅图像超分辨算法。迭代最小二乘法的整体优化过程极大地缩短了低分辨字典/高分辨字典的训练时间,它采用了与锚定邻域回归相同的优化规则,有效地保证了字典学习和图像重建在理论上的一致性。实验结果表明,所提算法的字典学习效果比K-均值奇异值分解(K-SVD)和Beta过程联合字典学习(BPJDL)等算法更高效,图像重建的效果也优于许多优秀的超分辨算法。

Single image super-resolution algorithm based on unified iterative least squares regulation

Machine learning based image Super-Resolution (SR) has been proved to be a promising single-image SR technology, in which sparseness representation and dictionary learning has become the hotspot. Aiming at the time-consuming dictionary training and low-accuracy SR recovery, an SR algorithm was proposed from the perspective of reducing the inconsistency between Low-Resolution (LR) feature and High-Resolution (HR) feature spaces as far as possible. The authors adopted Iterative Least Squares Dictionary Learning Algorithm (ILS-DLA) to train LR/HR dictionaries and Anchored Neighborhood Regression (ANR) to recover HR images. ILS-DLA was able to train LR/HR dictionaries in relatively short time because of its integral optimization procedure, by adopting the same optimization strategy of ANR, which theoretically reduced the diversity between LR/HR dictionaries effectively. A large number of experiments show that the proposed method achieves superior dictionary learning to K-means Singular Value Decomposition (K-SVD) and Beta Process Joint Dictionary Learning (BPJDL) algorithms etc., and provides better image restoration results than other state-of-the-art SR algorithms.